Contributions:
The contribution of this work is threefold:
First, we extend the mobile adversary model
of one-to-one mobile behavior to an epidemic case.
In our previous work [1]},
a malicious agent moves to another agent and leaves a
corrupted value.
The agent that just recovered is considered as a cured agent.
Note however that such an agent may have a corrupted value
and hence should be treated as a malicious one for another round
so as to apply a protocol to adjust its value.
Second, we introduce an intervention measure to the epidemic model
to enhance the resiliency of the consensus protocols
employing the modified MSR algorithms.
In conventional epidemic works such as [2]},
the intervention takes the form of the ratio at which
the general public should avoid contacts with others.
In this work, we relate such intervention ratio
with the parameters employed in MSR algorithms
for pruning the neighbors' values;
we formally analyze their role in resilient consensus.
Third, based on the modified MSR algorithms
used for mobile malicious models, we propose
two protocols with static and adaptive policies
for the intervention ratio globally announced
and hence the the pruning parameters locally
at the agents.
For both protocols, we characterize the graph conditions
and tolerable pandemic level under which resilient consensus
can be guaranteed.
Compared with the conventional mobile malicious model,
the epidemic malicious model is more powerful since it
has dynamic malicious agents. The pruning number
is no longer given, but has to be chosen properly
according to the infection level at the time.
The existence of pruning numbers that can guarantee
resilient consensus in a given graph is also discussed.
